Estimating Image Degradation Model Using GAN

The rise in use of visual data in many aspects of life encourage the demand for high-quality, sharp images. While technology of producing high-quality images has made a lot of progress, images still suffer from different un-known degradations that can occur from optics, bad weather/lighting scenarios, etc.
The deblurring task is been one of the most attracting ones in academics and industry. Many past works tried successfully to reduce image blurriness after image was taken.
This work tries to harness the power deep learning with generative models and use state-of-the-art architecture of GAN to mimic image degradation kernels. More specifically, it tries to learn those degradations from unpaired data and single image scenario, and then using those kernels to reconstruct sharp image from the degraded one. That, under the assumption that once the degradation is reproduced, there are well known methods of reconstructing sharp image using the degrading kernel.